The use of data compression has become increasingly important for information communication and storage given the now massive amounts of information communicated and stored electronically world-wide. By more effectively compressing data, bandwidth in communication channels and/or memory space in storage systems can be freed, allowing increased communication and/or storage capacity respectively.
The ISO/IEC 10918-1 standard, commonly referred to as JPEG has become probably the most popular form of image compression. However, JPEG becomes increasingly less effective for lossy compression at ratios exceeding approximately 30:1.
A compression method that results in improved quality of image restoration over JPEG compression for compression ratios exceeding 30:1 has been presented in C. Amerijckx et al. “Image compression by self-organized kohonen map”, IEEE Transactions on Neural Networks, vol. 9, no. 3, May 1998. This method involves transforming the image data using the discrete cosine transform (DCT), vector quantizing the DCT coefficients by a topological self-organising map (Kohonen map), differential coding by a first order predictor and entropic encoding of the differences.
In the specification of U.S. Pat. No. 5,950,146, the use of support vectors in combination with a neural network is described to assist in overcoming the problem of exponential increases in processing requirements with linear dimensional increases in estimations of functions. The method involves providing a predetermined error tolerance, which is the maximum amount that an estimate function provided by a neural network may deviate from the actual data.
The advantage of using support vector machines to alleviate the problem of disproportionate computational increases for dimensional increases for image compression was identified in: J. Robinson and V. Kecman, “The use of support vectors in image compression”, Proceedings of the 2nd International Conference on Engineering Intelligent Systems, University of Paisley, June 2000. The use of support vector machines goes some way to providing improved image quality relative to that obtained from JPEG compression for compression ratios greater than 30:1.
Neural networks have also been used for image compression; see for example the specification of U.S. Pat. No. 5,005,206. After the image is defined by a suitable function, which is typically a transformation of the image data into a different domain, a neural network is trained on the function to produce an estimate of the function. The image is reconstructed from the weights of the neural network.
A problem with neural networks is that for highly varying data, the error of the resulting estimate may be substantial. To reduce the error, a large number of points may be considered. However, this increases the computational burden and decreases compression. Even using support vectors, the computational burden to enable a solution to be computed within a required error may be prohibitively high.
Thus, it is an object of the present invention to provide a method, apparatus and/or software product that provide improved accuracy in data compression and/or in function estimation over existing methods, apparatus and software for comparative processing effort, or at least to provide the public with a useful alternative.
Further objects of the present invention may become apparent from the following description.
Any discussion of the prior art throughout the specification should in no way be considered as an admission that such prior art is widely known or forms part of common general knowledge in the field.